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Data Visualization MCP Server

by markomitranic
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Python
Hybrid

Data Visualization MCP Server

Overview

A Model Context Protocol (MCP) server implementation that provides the LLM an interface for visualizing data using Vega-Lite syntax.

Related MCP server: mcp-plots

Components

Tools

The server offers two core tools:

  • save_data

    • Save a table of data agregations to the server for later visualization

    • Input:

      • name (string): Name of the data table to be saved

      • data (array): Array of objects representing the data table

    • Returns: success message

  • visualize_data

    • Visualize a table of data using Vega-Lite syntax

    • Input:

      • data_name (string): Name of the data table to be visualized

      • vegalite_specification (string): JSON string representing the Vega-Lite specification

    • Returns: If the --output_type is set to text, returns a success message with an additional artifact key containing the complete Vega-Lite specification with data. If the --output_type is set to png, returns a base64 encoded PNG image of the visualization using the MPC ImageContent container.

Usage with Claude Desktop

# Add the server to your claude_desktop_config.json
{
  "mcpServers": {
    "datavis": {
        "command": "uv",
        "args": [
            "--directory",
            "/absolute/path/to/mcp-datavis-server",
            "run",
            "mcp_server_vegalite",
            "--output-type",
            "png" # or "text"
        ]
    }
  }
}

Usage with uv

uv --directory /Users/markomitranic/Sites/mcp/mcp-vegalite-server run mcp_server_vegalite --output-type png

Usage with Docker

docker build -t mcp-server-vegalite .
docker run -i --rm mcp-server-vegalite --output-type png

Available Tools

2 tools
save_dataB

A tool which allows you to save data to a named table for later use in visualizations. When to use this tool:

  • Use this tool when you have data that you want to visualize later. How to use this tool:

  • Provide the name of the table to save the data to (for later reference) and the data itself.

ParametersJSON Schema
NameRequiredDescriptionDefault
nameYesThe name of the table to save the data to
dataYesThe data to save

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it states the tool 'saves data' (implying a write operation), it lacks critical details: whether this requires specific permissions, if it overwrites existing tables, what happens on failure, or any rate limits. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and well-structured with clear sections ('When to use this tool' and 'How to use this tool'), making it easy to scan. Every sentence contributes to understanding the tool's purpose and usage, though it could be slightly more concise by integrating the sections more fluidly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 2 parameters with full schema coverage and no output schema, the description adequately covers the basic purpose and usage. However, as a mutation tool with no annotations, it lacks details on behavioral aspects like error handling, permissions, or side effects, which are important for contextual completeness. This makes it minimally viable but with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already documents both parameters ('name' and 'data') thoroughly. The description adds minimal value beyond the schema by restating parameter purposes in the 'How to use this tool' section. This meets the baseline of 3 when the schema does the heavy lifting, but doesn't provide additional semantic context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'save data to a named table for later use in visualizations.' It specifies the verb ('save'), resource ('data'), and intended use ('for later use in visualizations'), which is clear and actionable. However, it doesn't explicitly differentiate from its sibling 'visualize_data' beyond implying a sequential relationship.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes a 'When to use this tool' section that provides clear context: 'Use this tool when you have data that you want to visualize later.' This gives explicit guidance on the tool's purpose. However, it doesn't specify when NOT to use it or mention alternatives (e.g., if there are other ways to store data), which prevents a perfect score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

visualize_dataA

A tool which allows you to produce a data visualization using the Vega-Lite grammar. When to use this tool:

  • At times, it will be advantageous to provide the user with a visual representation of some data, rather than just a textual representation.

  • This tool is particularly useful when the data is complex or has many dimensions, making it difficult to understand in a tabular format. It is not useful for singular data points. How to use this tool:

  • Prior to visualization, data must be saved to a named table using the save_data tool.

  • After saving the data, use this tool to visualize the data by providing the name of the table with the saved data and a Vega-Lite specification.

ParametersJSON Schema
NameRequiredDescriptionDefault
data_nameYesThe name of the data table to visualize
vegalite_specificationYesThe vegalite v5 specification for the visualization. Do not include the data field, as this will be added automatically.

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it requires data to be saved first (a prerequisite), specifies that Vega-Lite v5 is used, and notes that the data field is automatically added (avoiding duplication). However, it lacks details on error handling, output format, or performance considerations, which would be helpful for a visualization tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and appropriately sized: it starts with a clear purpose statement, followed by bullet-pointed sections for usage guidelines and instructions. Each sentence adds value without redundancy, and the information is front-loaded for quick understanding. The format is efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (visualization with Vega-Lite), no annotations, and no output schema, the description does a good job of covering essential context: purpose, usage scenarios, prerequisites, and basic parameter semantics. However, it lacks details on what the visualization output looks like (e.g., image format, display method) and error cases, which would improve completeness for an agent invoking this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds minimal value beyond the schema: it mentions that data_name refers to 'the name of the table with the saved data' and vegalite_specification is 'a Vega-Lite specification,' but these are largely redundant with schema descriptions. No additional syntax, examples, or constraints are provided, meeting the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'produce a data visualization using the Vega-Lite grammar.' It specifies the verb ('produce') and resource ('data visualization'), but does not explicitly differentiate from its sibling tool 'save_data' beyond mentioning it as a prerequisite. The purpose is specific and actionable, though sibling differentiation is only implied through workflow dependency.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidelines in a structured format: 'When to use this tool' lists scenarios (visual representation for complex/multi-dimensional data, not for singular data points) and 'How to use this tool' outlines prerequisites (save data first with save_data) and steps. It clearly distinguishes when to use this tool versus alternatives by stating it's not useful for singular data points and requires prior data saving.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server
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TDQS

A3.6/5.0
Disambiguation5/5

The two tools have completely distinct purposes with no overlap. 'save_data' is for storing data in a table, while 'visualize_data' is for creating visualizations from saved data. The descriptions clearly differentiate their functions and usage contexts.

Naming Consistency5/5

Both tools follow a consistent verb_noun naming pattern ('save_data' and 'visualize_data'). The naming style is uniform throughout, using snake_case with clear action-object pairs that accurately reflect their functions.

Tool Count2/5

With only 2 tools, this server feels severely under-scoped for a data visualization domain. A complete visualization workflow would typically require tools for data manipulation, chart type selection, configuration adjustments, or exporting visualizations. The current set is too minimal for effective agent use.

Completeness2/5

The tool surface has significant gaps for a data visualization server. There are no tools for data transformation, filtering, or aggregation before visualization. Missing are tools for different visualization types, chart customization, or exporting results. The dependency on Vega-Lite specifications without helper tools creates a steep learning curve for agents.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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